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In this chapter, we examine whether corporate environmental reporting (CER) by listed companies in Kenya improves stock liquidity. The investigation is motivated by the growing interest by corporations, investors, and regulators toward embracing ecological protection with a view to creating sustainable societies for the future.

Using a panel dataset comprising of 244 firm-year observations from 50 listed firms in Kenya over a five-year period (2011 to 2015), we perform fixed-effects regressions to discern whether CER is associated with stock liquidity. To examine this, we utilize bid-ask (as well as quoted) spreads measured over month −9 to month +3 relative to a firm’s year end.

Despite the seemingly low levels of CER across firms in the sample (average: 32.6%), the results depict that CER is positively associated with stock liquidity. The results are robust even when we consider changes in bid-ask spreads and CER together with the other variables. The same results emerge when we study the association between bid-ask spreads and each CER item at a time over the period 2011–2015.

The results imply that listed companies in Kenya that engage in higher CER seem to be more attractive to investors. The higher CER seems to improve the information environment, hence reducing information asymmetry and therefore attracting investors. The results provide some evidence of positive economic consequences of engaging in additional disclosure over and above the traditional corporate financial reporting.

The study adds onto the dearth of literature on the economic consequences of embracing additional disclosure frameworks in developing countries where the adoption of alternative reporting frameworks is at infancy.

We discuss the device applications of a new impact ionization model. This model is based on a new formulation of the impact ionization rate for bulk semiconductors, derived from solvable high‐field Boltzmann transport equations. The model inputs are relaxation times which simulate the dominant electron‐phonon scatterings and are calibrated by realistic Monte Carlo simulations. Our impact ionization model is shown to be physically motivated and is easily implemented in the standard hydrodynamic device simulators HFIELDS and FIELDAY. An efficient numerical scheme is used to simulate three thin‐base silicon bipolar transistors. Results based on this impact ionization model are found to agree well with the experimental multiplication factors over a large range of applied voltages. These results are contrasted with the more phenomenological treatment of Scholl and Quade which is shown to be a low‐field limit of our model.

An energy transport model has been numerically implemented in the device simulator HFIELDS. The transport parameters for the standard hydrodynamic model and the energy transport model are calibrated by means of DAMOCLES, a two‐dimensional Monte Carlo Boltzmann equation solver. We analyse the relative merits of these two models by comparing their predictions of the energy and velocity distributions for a bipolar transistor and a ballistic diode. In the cases presented, the hydrodynamic model is found to agree with the Monte Carlo results more closely than the energy transport model.

This study reviews the use of meta-analysis in accounting research. We categorize the meta-analytic research into five topics: financial reporting, auditing, corporate governance and accounting quality, management accounting, and miscellaneous topics. Further, we classify the studies by the meta-analysis technique employed: Hunter et al. (1982), Hunter and Schmidt (2000), Lipsey and Wilson (2001), and Stouffer’s approach. We identify 27 meta-analytical studies over the period 1985–2014 with financial reporting (auditing) topics representing seven (six) of these studies. Our review highlights that meta-analytic methods are being applied and accepted, more frequently, to answer complex questions concerning the moderating effects of country-level variables, such as national culture, economic conditions, and institutional characteristics, on various associations of interest.

The purpose of this paper is to document the relation between the bank’s regional CEO’s emotional bias (optimism and loss aversion) and the delegation of decision rights to the account manager.

The partial least squares (PLS) method is applied to investigate the degree to which bank’s regional CEO delegate decisions and the circumstances that drive variation in delegation.

The results show that delegation does not appear to be monolithic; instead, the results show that delegation varies with the personal characteristics of the bank’s regional CEO.

Banks are invited to take into account the effect of the emotional biases of the directors on the delegation of its power.

The authors put forward an original effort that is intended to discuss in particular the effect of psychological biases on the decentralization of the decision-making rights.

The purpose of this paper is to deal with the investigation of no-load operation of tubular linear permanent magnet synchronous machines (T-LPMSMs). It is aimed at the prediction of the phase flux linkages, the back-EMF and the cogging force using a position varying magnetic equivalent circuit (MEC).

This study is based on the elaboration and the resolution of the position varying MEC, and the utilization of its results for the prediction of the phase flux linkages, the back-EMF and the cogging force, considering a general topology of T-LPMSMs. Then, a case study is treated with a position varying MEC-based investigation of its no-load features. These are validated by a 2-D finite element analysis (FEA).

It has been found that the developed position varying MEC can be regarded as an accurate tool that requires a low CPU-time.

Beyond the FEA validation, this work should be extended to an experimental one. Moreover, the position varying MEC validity should be extended to load operation in order to enable the prediction of the force production capability.

The developed position varying MEC could be suitably used for the pre-design of T-LPMSMs. These are currently given an increasing attention in many applications, such as wave energy conversion and free-piston engines.

The paper proposes a position varying MEC for the prediction of the features of T-LPMSMs.

Electrical potentials in a junction field transistor can be calculated using a simplified model based on a complete depletion assumption. This gives rise to a free boundary problem. We show here how we can approximate this problem with a quasi‐variational inequality technique and the shape optimization method. A detailed analysis of these methods is presented. Using some numerical experiments we compare our results with the solution of the discrete drift‐diffusion system, accomplished with a Gummel‐like algorithm. The numerical results suggest that the methods proposed here work successfully and that the shape optimization technique provides a reasonably free boundary without excessive iterations.

The purpose of this paper is to meta‐analyze the results of 22 empirical studies that examine the relationship between voluntary disclosure and cost of equity capital. The authors examine whether differences in results are attributable to moderating effects related to disclosure environment, the measurement of the disclosure score and the proxy used to measure the cost of equity capital.

The approach used is the meta‐analysis statistic technique developed by Hunter et al.

The results emphasize the need to explicitly consider the legal and institutional aspects (high disclosure environment versus low disclosure environment) when one analyzes the association between disclosure level and cost of equity capital.

Since the authors' analysis confirms the negative association between disclosure and cost of equity capital in countries characterized by low disclosure environment, managers in these contexts are encouraged to make more voluntary disclosure in order to reduce uncertainty among investors and increase the marketability of their securities.

A systemic risk measure is proposed accounting for links and mutual dependencies between financial institutions utilizing tail event information. Financial Risk Meter (FRM) is based on least absolute shrinkage and selection operator quantile regression designed to capture tail event co-movements. The FRM focus lies on understanding active set data characteristics and the presentation of interdependencies in a network topology. Two FRM indices are presented, namely, FRM@Americas and FRM@Europe. The FRM indices detect systemic risk at selected areas and identify risk factors. In practice, FRM is applied to the return time series of selected financial institutions and macroeconomic risk factors. The authors identify companies exhibiting extreme “co-stress” as well as “activators” of stress. With the SRM@EuroArea, the authors extend to the government bond asset class, and to credit default swaps with FRM@iTraxx. FRM is a good predictor for recession probabilities, constituting the FRM-implied recession probabilities. Thereby, FRM indicates tail event behavior in a network of financial risk factors.